Fault detection of slewing bearings in engineering cranes based on adaptive algorithms
1
Zhejiang Guangsha Vocational and Technical University of Construction, Zhejiang, Dong Yang, 322100, China
2
Hengyang Valin Steel Pipe Co., Ltd., Hunan, Hengyang, 421001, China
Submission date: 2025-12-09
Final revision date: 2026-03-02
Acceptance date: 2026-03-15
Online publication date: 2026-03-17
Publication date: 2026-03-17
Corresponding author
Tiejun Liu
Zhejiang Guangsha Vocational and Technical University of Construction, Zhejiang, Dong Yang, 322100
Zhejiang Guangsha Vocational and Technical University of Construction, Zhejiang, Dong Yang, 322100
KEYWORDS
TOPICS
ABSTRACT
Slewing bearings play a crucial role in the operational efficiency and security of engineering cranes by supporting rotational movements under heavy loads. Over time, these components wear and degrade, making early fault detection critical to avoiding mechanical failures, costly downtime, and security risks. Conventional condition monitoring methods frequently struggle with inconsistent data patterns, sensor noise, and dynamic operating conditions. There is an urgent need for intelligent, adaptive fault detection mechanisms that can precisely predict slewing bearing failures under varying load and operational circumstances. This study aims to build a robust, adaptive fault detection algorithm—Slewing Bearing Fault Detection (SBFDetect)—capable of identifying early signs of faults in slewing bearings using real-time sensor data. The goal is to improve maintenance planning and reduce unexpected failures in engineering cranes. A dataset called the Slewing Bearings Fault (SBF) Dataset was created, which includes key parameters such as vibration intensity, temperature, noise levels, rotation speed, load pressure, lubrication levels, metal debris levels, hours of operation, and sensor drift.
ACKNOWLEDGEMENTS
The authors sincerely thank all contributors who supported the creation of the SBFDetect algorithm, especially technical staff, data providers, and reviewers, whose insights significantly improved the quality of this study.
FUNDING
This research received no external funding.
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